Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Visual Odometry Based Omni-directional Hyperlapse Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Patch Similarity in Transform Domain for Intensity/Range Image Denoising with Edge Preservation

Authors : Seema Kumari, Srimanta Mandal, Arnav Bhavsar

Published in: Computer Vision, Pattern Recognition, Image Processing, and Graphics

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

For the image denoising task, the prior information obtained from grouping similar non-local patches has been shown to serve as an effective regularizer. Nevertheless, noise may create ambiguity in grouping similar patches, hence it may degrade the results. However, most of the non-local similarity based approaches do not take care of the issue of noisy grouping. Hence, we propose to denoise an image by mitigating the issue of grouping non-local similar patches in presence of noise in transform domain using sparsity and edge preserving constraints. The effectiveness of the transform domain grouping of patches is utilized for learning dictionaries, and is further extended for achieving an initial approximation of sparse coefficient vector for the clean image patches. We have demonstrated the results of effective grouping of similar patches in denoising intensity as well as range images.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter GMM Based Single Depth Image Super-Resolution
next chapter Multi-modal Image Analysis for Plant Stress Phenotyping
Footnotes
1
Note that the mean intensity values of patches are used on for this particular demonstration, for simplicity of interpretation. In our actual approach we use the vectorized patch values compute patch similarity using l2 norms.
 
Literature
1.
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D Nonlinear Phenom. 60(1–4), 259–268 (1992)MathSciNetCrossRef
2.
Levin, A., Weiss, Y., Durand, F., Freeman, W.: Efficient marginal likelihood optimization in blind deconvolution. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2657–2664, June 2011
3.
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)MathSciNetCrossRef
4.
5.
Dong, W., Zhang, L., Shi, G., Li, X.: Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620–1630 (2013)MathSciNetCrossRef
6.
Buades, A., Coll, B., Morel, J.: A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4(2), 490–530 (2005)MathSciNetCrossRef
7.
Katkovnik, V., Foi, A., Egiazarian, K., Astola, J.: From local kernel to nonlocal multiple-model image denoising. Int. J. Comput. Vis. 86(1), 1–32 (2010)MathSciNetCrossRef
8.
Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Non-local sparse models for image restoration. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2272–2279, September 2009
9.
Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 479–486 (2011)
10.
Mandal, S., Kumari, S., Bhavsar, A., Sao, A.K.: Multi-scale image denoising while preserving edges in sparse domain. In: 2016 6th European Workshop on Visual Information Processing (EUVIP), pp. 1–6, October 2016
11.
Hu, W., Li, X., Cheung, G., Au, O.: Depth map denoising using graph-based transform and group sparsity. In: IEEE International Workshop on Multimedia Signal Processing (MMSP), pp. 001–006. IEEE (2013)
12.
Jiao, J., Yang, Q., He, S., Gu, S., Zhang, L., Lau, R.W.: Joint image denoising and disparity estimation via stereo structure PCA and noise-tolerant cost. Int. J. Comput. Vis. 124(2), 1–19 (2017). Please check and confirm if the inserted details for Ref. [12] is correctMathSciNetCrossRef
13.
Daubechies, I., Defrise, M., De Mol, C.: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun. Pure Appl. Math. 57(11), 1413–1457 (2004)MathSciNetCrossRef
14.
Zontak, M., Mosseri, I., Irani, M.: Separating signal from noise using patch recurrence across scales. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1195–1202 (2013)
15.
Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: IEEE International Conference on Computer Vision (ICCV), pp. 479–486, November 2011
16.
Scharstein, D., Pal, C.: Learning conditional random fields for stereo. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8, June 2007
17.
Hirschmuller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8, June 2007
18.
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRef
19.
He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)CrossRef
Metadata
Title
Patch Similarity in Transform Domain for Intensity/Range Image Denoising with Edge Preservation
Authors
Seema Kumari
Srimanta Mandal
Arnav Bhavsar
Copyright Year
2018
Publisher
Springer Singapore
Book
Computer Vision, Pattern Recognition, Image Processing, and Graphics

Print ISBN: 978-981-13-0019-6

Electronic ISBN: 978-981-13-0020-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-13-0020-2_23

Premium Partner

    Image Credits